CN107064731A - Fault Section Location of Distribution Network based on adaptive chaos drosophila optimized algorithm - Google Patents
Fault Section Location of Distribution Network based on adaptive chaos drosophila optimized algorithm Download PDFInfo
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- CN107064731A CN107064731A CN201710108957.1A CN201710108957A CN107064731A CN 107064731 A CN107064731 A CN 107064731A CN 201710108957 A CN201710108957 A CN 201710108957A CN 107064731 A CN107064731 A CN 107064731A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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Abstract
The invention discloses a kind of Fault Section Location of Distribution Network based on adaptive chaos drosophila optimized algorithm, it is related to distribution network system fault location field, distribution line failure point can not be found accurately, in time for prior art and causes the technical problem of trouble shooting time length, used：S1, using wave recording device each section of electric current in power distribution network is monitored, and each section of current detection value is deposited in array X with setting setting current value contrast generation fault message；S2, the equipment for seeking to break down by array X information, by constructing object function F, to the difference for describing distribution network feeder actual conditions with expecting failure situation；S3, utilize adaptive chaos drosophila optimized algorithm, minimum value to meeting the object function F, carries out optimizing in the array X, realizes the positioning to fault section, there is preferable fault-tolerant ability present invention can apply to solve extensive fault-location problem, and for power distribution network multipoint fault locating.
Description
Technical field
The present invention relates to distribution network system fault location field, more particularly to a kind of optimized based on adaptive chaos drosophila is calculated
The Fault Section Location of Distribution Network of method.
Background technology
In the whole power system of China, controller switching equipment is multi-point and wide-ranging, and distribution is link the weakest.It is used as receiving end system
The core of system, distribution be improve the vital material base of power supply reliability, be improve power network ability of preventing and fighting natural adversities,
Ensure the last barrier of terminal user's uninterrupted power supply.With the continuous improvement required distribution network reliability, distribution wire
The on-line monitoring on road will be imperative.Distribution network technology level and rack present situation are compared than major network, there is larger gap, mainly
Automatization level backwardness, rack weakness etc. are showed, once occurring distribution line failure, trouble shooting time is long, non-faulting region
It is difficult to restore electricity or complete in time to turn power supply, has had a strong impact on power supply reliability, production or resident living to electricity consumption enterprise
Bring safety and property hidden danger.
The content of the invention
The present invention provides a kind of Fault Section Location of Distribution Network based on adaptive chaos drosophila optimized algorithm, is used to
Distribution line failure point can not be found accurately, in time and cause the technical problem of trouble shooting time length by solving prior art.
To solve the above problems, the present invention adopts the following technical scheme that realization：
A kind of Fault Section Location of Distribution Network based on adaptive chaos drosophila optimized algorithm, comprises the following steps：
S1, using wave recording device each section of electric current in power distribution network is detected, and by each section of current detection value and set whole
Determine current value contrast generation fault message to deposit in array X；
S2, the equipment for seeking to break down by array X information, by constructing object function F, to describe distribution feedback
Line actual conditions and the difference for expecting failure situation；
S3, utilize adaptive chaos drosophila optimized algorithm, the minimum value to meeting the object function F, in the array X
Middle carry out optimizing, realizes the positioning to fault section.
Preferably, the wave recording device of the step S1 is transient state wave recording device；
The fault message of the step S1 includes：When each section of current detection value is identical with setting setting current value, take
It is worth for 0, different then value is 1；
The step S2 constructs object function F：Wherein X (j) is described
Array X j-th of element, represents measuring control point j actual condition value, and 0 is disconnects, and 1 is normal, and Y (j) is the jth of the array Y
Individual element, represents measuring control point j expectation state value, and μ is weight coefficient according to the practical problem value to be solved, span 0~1
Between,Faulty equipment sum is represented, Z (j) is the quantity of faulty equipment at the point j of observing and controlling, and N is the sum of measuring control point
Amount.
Preferably, adaptive chaos drosophila optimized algorithm is utilized in the step S3, to meeting the object function F most
Small value, carries out optimizing in the array X, comprises the following steps that：
S301, initiation parameter, population size Sizepop, greatest iteration number Maxgen, drosophila group position X_axis,
Y_axis, fitness (flavor concentration) variance threshold values δ, chaos traversal number of times M；
S302, imparting drosophila individual are using the random direction and distance of smell search of food, and RandomValue is Search Length
From：
S303, first estimation drosophila individual are with origin apart from Disti：New position is calculated again
Flavor concentration decision content Si：Si=1/Disti；
S304, by flavor concentration decision content SiFlavor concentration decision function (or being fitness function) is substituted into, for asking
Go out the flavor concentration Smell of drosophila body positioni：Smelli=Function (Si)；
S305, find out optimal drosophila (the optimum individual) [bestSmell of flavor concentration in the drosophila colony
Bestindex]=min (Smelli)；
S306, record and retain best flavors concentration value bestSmell and its X, Y-coordinate, at this time drosophila colony utilizes
Vision flies to the X, Y-coordinate：
S307, basisCalculate the average taste concentration of the drosophila colony
Smellavg(average fitness), according toCalculate the drosophila colony flavor concentration variances sigma2
(fitness variance)；
If S308, σ2＜ δ and M ＞ 0, then by drosophila body position Xi、YiChaos technology is mapped by Logistic to be converted into
Drosophila individual new position X ' in search spacei、Y′i, M=M-1, otherwise, jump to step S312 execution；
S309, first calculating new position X 'i、Y′iWith the distance of originFlavor concentration judgement is calculated again
Value S 'i=1/Dist 'i；
S310, by flavor concentration decision content S 'iFlavor concentration decision function is substituted into, the taste for obtaining drosophila body position is dense
Spend Smell 'i=Function (S 'i)；
If S311, Smell 'i＜ Smellbest, then Smellbest=Smell 'i, X_axis=X 'i, Y_axis=Y
′i, step S308 is then branched to, step S308 is otherwise passed directly to；
S312, repeat step S302~S311 and be iterated optimizing, until current iteration number of times is equal to greatest iteration
Number Maxgen has reached precision target call.
Preferably, also include after step S3：S4, for having in power distribution network in the case that multiple power supplys are powered,
When feeder line has at one and many places are broken down, it is considered to which information transfer has the undistorted influence brought for positional accuracy, and
Carry out simulation analysis.
The present invention has the following effects that：The present invention is used as a kind of power distribution network event based on adaptive chaos drosophila optimized algorithm
Hinder location Calculation analysis method, can be applied to solve extensive fault-location problem, and for power distribution network multipoint fault locating have
Preferable fault-tolerant ability.
Brief description of the drawings
Fig. 1 is the step flow chart of the present invention；
Fig. 2 is the Complicated Distribution Network network constructive embodiment schematic diagram that the present invention is provided.
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached in the embodiment of the present invention
Figure, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is the present invention
A part of embodiment, rather than whole embodiments.The embodiments of the invention being generally described and illustrated herein in the accompanying drawings
The scope for being not intended to limit claimed invention is described in detail, but is merely representative of the selected embodiment of the present invention.Base
Embodiment in the present invention, those of ordinary skill in the art obtained under the premise of creative work is not made it is all its
His embodiment, belongs to the scope of protection of the invention.
As depicted in figs. 1 and 2, the present invention utilizes curent change of the transient state wave recording device installed in power distribution network to installation place
It is monitored, and the discrete data that it is made up of with setting setting current contrast generation 0 and 1, deposited in as fault message
In array X, by constructing object function F, to describe distribution network feeder actual conditions and expect the difference of failure situation, and utilize
Adaptive chaos drosophila optimized algorithm, the minimum value to meeting object function F carries out optimizing in information array X, realizes a pair event
Hinder the positioning of section.In the case of thering are multiple power supplys to be powered in power distribution network, when feeder line has at one and many places
During failure, it is considered to which information transfer has the undistorted influence brought for positional accuracy, and carries out simulation analysis.
To achieve these goals, the technical scheme of the present embodiment comprises the following steps：
S1, using transient state wave recording device electric current in power distribution network is monitored, and itself and setting setting current contrast are given birth to
Into the discrete data being made up of 0 and 1, deposited in as fault message in array X；
S2, the information by array X, seek the equipment broken down, by constructing object function F, to describe distribution
Feeder line actual conditions and the difference for expecting failure situation；
S3, in the application stage, based on the object function of above-mentioned foundation, using adaptive chaos drosophila optimized algorithm, to full
Foot-eye function F minimum value, carries out optimizing in information array X, realizes the positioning to fault section；
S4, for having in power distribution network in the case that multiple power supplys are powered, when feeder line have at one and many places occur therefore
During barrier, it is considered to which information transfer has the undistorted influence brought for positional accuracy, and carries out simulation analysis.
In step sl, fault message array X includes：
Installation place curent change is monitored by transient state wave recording device, represents that equipment working state is normal at monitoring with 0,1
Represent that this goes out to monitor fault current, show that feeder line breaks down；
In step s 2, it is specific as follows for object function F foundation：
1. for simple power distribution network, the object function constructedWherein X
(j) j-th of element for being fault message array X, represents measuring control point j actual condition value, and 0 is disconnects, and 1 is normal；Y (j) is
Array Y j-th of element, represents measuring control point j expectation state value.In order to avoid occurring that one is added in wrong diagnosis, above formula,
Wherein μ is weight coefficient, and according to the practical problem value to be solved, scope is between [0,1]；Represent that faulty equipment is total
Number, Z (j) is the quantity of faulty equipment at the point j of observing and controlling, and N is the total quantity of measuring control point.
2. for the higher power distribution network of complexity, the object function F constructed is needed the flow direction consideration of mesh current
Enter, the distribution network failure orientation problem to handle operation with closed ring.It is assumed that being powered by one section of power supply to the whole network, in network
Power flow outgoing direction is the positive direction of feeder line, i.e., the positioning of operation with closed ring distribution network failure is converted into simple distribution network failure determines
Position, sets up unified object function for F=p1·(F1(n→m)+F1(m→n))+p2·F2, wherein：F1(n→m)Represent the distribution in region 1
Net is individually powered by n ends and network positive direction is set as object function during from n to m, and wherein n and m are monitoring distribution feedbacks
The first and last end of line；Same F1(m→n)Power distribution network for region 1 is individually powered by m ends and network positive direction is set as from m to n
When object function；F2Represent the object function in region 2；When the node in region 1, which is monitored to, to break down, p1For 1, nothing
It is 0, p when failure occurs2Value condition is similar.
In step s3, adaptive chaos drosophila optimized algorithm used, the minimum value optimizing to meeting object function F, tool
Body step is as follows：
S301, initiation parameter：Population size Sizepop, greatest iteration number Maxgen, drosophila group position X_axis,
Y_axis, fitness (flavor concentration) variance threshold values δ, chaos traversal number of times M.
S302, imparting drosophila individual are using the random direction and distance of smell search of food, and RandomValue is Search Length
From：
S303, due to that can not learn food position, therefore first estimation and the distance of originCalculate again
The flavor concentration decision content S of new positioni=1/Disti, this value be apart from inverse；
S304, flavor concentration decision content SiFlavor concentration decision function (or being fitness function) is substituted into, for obtaining
The flavor concentration Smell of drosophila body positioni=Function (Si)：
S305, find out the optimal drosophila (optimum individual) of flavor concentration in the drosophila colony：[bestSmell
Bestindex]=min (Smelli)；
S306, record and retain best flavors concentration value bestSmell and its X, Y-coordinate, at this time drosophila colony utilizes
Vision flies to the position：
S307, basisCalculate the average taste concentration of the drosophila colony
Smellavg(average fitness), according toCalculate the drosophila colony flavor concentration variances sigma2
(fitness variance)；
If S308, σ2＜ δ and M ＞ 0, then by drosophila body position Xi, YiChaos technology is mapped by Logistic to be converted into
Drosophila individual new position X ' in search spacei, Y 'i, M=M-1；Otherwise step S312 execution is jumped to；
S309, first estimation new position X 'i, Y 'iWith the distance of originFlavor concentration judgement is calculated again
Value S 'i=1/Dist 'i；S310, by flavor concentration decision content S 'iFlavor concentration decision function is substituted into, drosophila body position is obtained
's
Flavor concentration Smell 'i=Function (S 'i)；
If S311, Smell 'i＜ Smellbest, then Smellbest=Smell 'i, X_axis=X 'i, Y_axis=Y
′i, step S308 is then gone to, step S308 is otherwise passed directly to；
S312, into iteration optimizing, repeat step S302~S311, until current iteration number of times be equal to greatest iteration
Number Maxgen has reached precision target call.
In step s 4, under many power supply complex situations, when have in feeder line broken down at individual node when, it is considered to
Transmitting fault information has the undistorted influence brought for positional accuracy.It is specific as follows：
1. each lead-in circuit breaker, interconnection switch and feeder line sector number are carried out to Complicated Distribution Network network, and carries out related area
Domain search space dimensionality, population scale, maximum iteration setting etc. are set.
2. fault location emulation is carried out when Single Point of Faliure occurs for power distribution network.Assuming that the section broken down is region 1
L14With the L in region 223(node S18With node S27Upload information be distorted), test the algorithm whether there is in transmitting fault information it is abnormal
Become the influence brought for positional accuracy.
3. fault location emulation is carried out when multipoint fault occurs for power distribution network.Assuming that feeder line section L14、L16、L22And L23Together
When break down, test the algorithm is having undistorted influence (the node S brought for positional accuracy in transmitting fault information17
And S28The information of upload is distorted).
Specifically, illustrated so that the present invention is applied to certain Complicated Distribution Network network as an example：
Step 1：Needed first using adaptive chaos drosophila optimized algorithm positioning fault section each to Complicated Distribution Network network disconnected
Road device, switch and feeder line sector number, numbering result are as shown in Fig. 2 wherein SxxRepresent test point, LxxRepresent tested region.
Step 2：The design parameter emulated is set, and the search space dimension of region 1 is 8, and the search space dimension of region 2 is
7, it is 30, the end condition F of algorithm to set population scaleminValue continuously repeat appearance number of times t cannot be less than 20 or calculate reach
To maximum iteration 200.
Step 3：Emulated when there is individual node to break down on distribution feeder, it is assumed that the area broken down
Section is the L in region 114With the L in region 223, confirmatory emulation has been carried out to algorithm for whetheing there is upload information distortion, and draw
Accurate fault location is interval.
Step 4：Power distribution network multiple spot is broken down and emulated, it is assumed that section L14、L16、L22And L23Break down simultaneously.
Confirmatory emulation has been carried out to algorithm for whetheing there is upload information distortion, and has drawn accurate fault location interval (node S17
And S28The information of upload is distorted).
Power distribution network Single Point of Faliure and multipoint fault locating simulation result are as shown in table 1.
The power distribution network Single Point of Faliure of table 1 and multipoint fault locating simulation result
Analytical table 1, which can be obtained, such as draws a conclusion：
1. X data monitor for transient state wave recording device and pass through the fault message of processing generation in table；Optimal solution is to utilize
Adaptive chaos drosophila optimized algorithm successive ignition is calculated, and 1 represents the interval faulty generation of the feeder line, and 0 represents the feeder line
Section fault-free occurs, and for region 1, when the forward direction for flowing to network is set as from n to m and from m to n in the case of two kinds, counts
It is L to calculate gained optimal solution14, that is, show L14Section breaks down；For region 2, the information data the 3rd in optimal solution
Position is 1, and it is L to show fault section23, show simultaneously, when last position of information data of region 1 and the information data of region 2 the 6th
When being distorted, the algorithm updated using dynamic is solved, and can still be properly positioned fault section.
2. for region 1, when network, which flows to forward direction, to be set as from n to m, the event of the optimal solution calculated by algorithm
It is [00010000] to hinder information data, that is, indicates section L14Break down, when network, which flows to forward direction, to be set as from m to n, calculate
The fault information data of gained optimal solution is [00100000], indicates L16Section breaks down.For the power distribution network in region 2, most
It is excellent solution information data second and the 3rd all be 1, that is, show section L22And L23It is faulty to occur.From simulation result,
When the network direction of region 1 is set as from n to m, node S17With the node S in region 228When the data message of transmission is distorted,
The fault location effect of algorithm is not influenceed.
The method have the characteristics that：For distribution network failure positioning, using the monitoring to curent change, record in power distribution network
Current status, and consider distribution network failure orientation problem of the direction of fault current to handle operation with closed ring, thus build
Unified fitness function has been found, using the adaptive chaos drosophila optimized algorithm for considering chaotic characteristic, enhancing has been realized global
The purpose of search capability, preferably retains and strengthens the robustness of algorithm in itself.Occur Single Point of Faliure and multiple spot in power distribution network
In the case of two kinds of failure, and consider that whetheing there is upload information distortion has carried out confirmatory emulation to algorithm, and drawn accurate event
Barrier positioning is interval, it was demonstrated that this algorithm has preferable convergence and has good anti-interference.
Claims (4)
1. a kind of Fault Section Location of Distribution Network based on adaptive chaos drosophila optimized algorithm, it is characterised in that including
Following steps：
S1, using wave recording device each section of electric current in power distribution network is detected, and each section of current detection value and setting are adjusted into electricity
Flow valuve contrast generation fault message is deposited in array X；
S2, the equipment for seeking to break down by array X information, it is real to describe distribution network feeder by constructing object function F
Border situation and the difference for expecting failure situation；
S3, using adaptive chaos drosophila optimized algorithm, the minimum value to meeting the object function F is entered in the array X
Row optimizing, realizes the positioning to fault section.
2. according to the method described in claim 1, it is characterised in that：
The wave recording device of the step S1 is transient state wave recording device；
The fault message of the step S1 includes：When each section of current detection value then value identical with setting setting current value as
0, different then value is 1；
The step S2 constructs object function F：Wherein X (j) is j-th yuan of the array X
Element, represents measuring control point j actual condition value, and 0 is disconnects, and 1 is normal, and Y (j) is j-th of element of the array Y, represents to survey
Control point j expectation state value, μ is weight coefficient according to the practical problem value to be solved, between span 0~1,
Faulty equipment sum is represented, Z (j) is the quantity of faulty equipment at the point j of observing and controlling, and N is the total quantity of measuring control point.
3. according to the method described in claim 1, it is characterised in that：
Adaptive chaos drosophila optimized algorithm, the minimum value to meeting the object function F, described are utilized in the step S3
Optimizing is carried out in array X, is comprised the following steps that：
S301, initiation parameter, population size Sizepop, greatest iteration number Maxgen, drosophila group position X_axis, Y_
Axis, fitness (flavor concentration) variance threshold values δ, chaos traversal number of times M；
S302, imparting drosophila individual are using the random direction and distance of smell search of food, and RandomValue is detection range：
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S303, first estimation drosophila individual are with origin apart from Disti：The taste for calculating new position again is dense
Spend decision content Si：Si=1/Disti；
S304, by flavor concentration decision content SiFlavor concentration decision function (or being fitness function) is substituted into, for obtaining drosophila
The flavor concentration Smell of individual body positioni：Smelli=Function (Si)；
S305, find out the optimal drosophila (optimum individual) [bestSmell bestindex] of flavor concentration in the drosophila colony=
min(Smelli)；
S306, record and retain best flavors concentration value bestSmell and its X, Y-coordinate, at this time drosophila colony utilizes vision
Flown to the X, Y-coordinate：
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S307, basisCalculate the average taste concentration Smell of the drosophila colonyavgIt is (flat
Equal fitness), according toCalculate the drosophila colony flavor concentration variances sigma2(fitness side
Difference)；
If S308, σ2＜ δ and M ＞ 0, then by drosophila body position Xi、YiChaos technology is mapped by Logistic and is converted into search
Drosophila individual new position X ' in spacei、Y′i, M=M-1, otherwise, jump to step S312 execution；
S309, first calculating new position X 'i、Y′iWith the distance of originFlavor concentration decision content S ' is calculated againi
=1/Dist 'i；
S310, by flavor concentration decision content S 'iFlavor concentration decision function is substituted into, the flavor concentration of drosophila body position is obtained
Smell′i=Function (S 'i)；
If S311, Smell 'i＜ Smellbest, then Smellbest=Smell 'i, X_axis=X 'i, Y_axis=Y 'i, then
Step S308 is jumped to, step S308 is otherwise passed directly to；
S312, repeat step S302~S311 and be iterated optimizing, until current iteration number of times is equal to greatest iteration number
Maxgen has reached precision target call.
4. the method according to any one of Claim 1-3, it is characterised in that：Also include after step S3：S4, for
Have in power distribution network in the case that multiple power supplys are powered, when feeder line has at one and many places are broken down, it is considered to information transfer
There is the undistorted influence brought for positional accuracy, and carry out simulation analysis.
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| CN201710108957.1A CN107064731A (en) | 2017-02-27 | 2017-02-27 | Fault Section Location of Distribution Network based on adaptive chaos drosophila optimized algorithm |
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| CN201710108957.1A CN107064731A (en) | 2017-02-27 | 2017-02-27 | Fault Section Location of Distribution Network based on adaptive chaos drosophila optimized algorithm |
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